Multi-core and even multi-processor computers are increasingly common, even for home
use. This does not automatically bring better processing performance. When the machine is …

We study the use of massively parallel architectures for computing a matrix inverse. Two
different algorithms are reviewed, the traditional approach based on Gaussian elimination …

This paper presents a parallel algorithm for computing the inversion of a dense matrix based
on Gauss-Jordan elimination. The algorithm is proposed for the implementation on the linear …

We study two different techniques for the computation of a matrix inverse, the traditional
approach based on Gaussian factorization and the Gauss-Jordan elimination alternative …

In this paper we propose a faster variation of one-sided Jacobi algorithm. We bring the idea
of Fast-Givens rotation and utilize it in Jacobi algorithm to generate a so-called Fast …

This paper derives a set of new algorithms based on the exchange method for the
computation of matrix inverses including nonsingular, symmetric nonsingular, and …

We present a new parallel matrix inversion algorithm and report its implementation on
parallel computers with distributed memory. The algorithm features natural load balance …

We present two parallel strategies to compute the inverse of a dense matrix, based on the so-
called Sherman-Morrison algorithm and demonstrate their efficiency in memory and runtime …

We contribute two parallel strategies to compute the exact and approximate inverse of a
dense matrix, based on the so-called inverse Sherman-Morrison algorithm and demonstrate …

This paper describes techniques to compute matrix inverses by means of algorithms that are
highly suited to massively parallel com+ tation. In contrast, conventional techniques such as …